Non-parametric learning critical behavior in Ising partition functions: PCA entropy and intrinsic dimension

Abstract

This work studies non-parametric methods for learning critical behavior in Ising partition functions. It compares intrinsic dimension with PCA entropy and shows that PCA entropy can extract critical temperatures accurately from modest lattice sizes.

Hanlin Sun

Wallenberg Initiative on Networks and Quantum Information (WINQ) Research Fellow